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The etiologies of Parkinson's disease (PD) remain unclear. Some, such as certain genetic mutations and head trauma, are widely known or easily identified. However, these causes or risk factors do not account for the majority of cases. Other, less visible factors must be at play. Among these is a widely used industrial solvent and common environmental contaminant little recognized for its likely role in PD: trichloroethylene (TCE). TCE is a simple, six-atom molecule that can decaffeinate coffee, degrease metal parts, and dry clean clothes. The colorless chemical was first linked to parkinsonism in 1969. Since then, four case studies involving eight individuals have linked occupational exposure to TCE to PD. In addition, a small epidemiological study found that occupational or hobby exposure to the solvent was associated with a 500% increased risk of developing PD. In multiple animal studies, the chemical reproduces the pathological features of PD.Exposure is not confined to those who work with the chemical. TCE pollutes outdoor air, taints groundwater, and contaminates indoor air. The molecule, like radon, evaporates from underlying soil and groundwater and enters homes, workplaces, or schools, often undetected. Despite widespread contamination and increasing industrial, commercial, and military use, clinical investigations of TCE and PD have been limited. Here, through a literature review and seven illustrative cases, we postulate that this ubiquitous chemical is contributing to the global rise of PD and that TCE is one of its invisible and highly preventable causes. Further research is now necessary to examine this hypothesis.

BackgroundParkinson's disease (PD) is a chronic, disabling neurodegenerative disorder.ObjectiveTo predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests.MethodsParticipants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density.ResultsThe study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = -0.57, p

The gastrointestinal microbiome is altered in Parkinson's disease and likely plays a key role in its pathophysiology, affecting symptoms and response to therapy and perhaps modifying progression or even disease initiation. Gut dysbiosis therefore has a significant potential as a therapeutic target in Parkinson's disease, a condition elusive to disease-modifying therapy thus far. The gastrointestinal environment hosts a complex ecology, and efforts to modulate the relative abundance or function of established microorganisms are still in their infancy. Still, these techniques are being rapidly developed and have important implications for our understanding of Parkinson's disease. Currently, modulation of the microbiome can be achieved through non-pharmacologic means such as diet, pharmacologically through probiotic, prebiotic, or antibiotic use and procedurally through fecal transplant. Novel techniques being explored include the use of small molecules or genetically engineered organisms, with vast potential. Here, we review how some of these approaches have been used to date, important areas of ongoing research, and how microbiome modulation may play a role in the clinical management of Parkinson's disease in the future.

Autonomic nervous system involvement precedes the motor features of Parkinson's disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.

Background:It is unknown whether subjective daytime sleepiness or objective napping could precede the risk of Parkinson's disease (PD) in the long term. Methods:We studied 2920 men (mean age 76 years) without a history of PD and followed them for 11 years. Excessive daytime sleepiness (EDS) was defined as having an Epworth Sleepiness Scale score >10. Objective naps were defined as ≥5 consecutive minutes of inactivity as measured by actigraphy, and napping duration was the accumulated time of naps outside the main sleep period. We used logistic regression to compare PD risk across four groups: no EDS& napping